The production of milk by ungulate species facilitates the dairy and meat industries. This is most obvious for the production of dairy products, primarily through the Bovidae family; however, milk production also facilitates growth in other production animals, such as swine, where increased milk production results in more efficient growth of offspring.
The effects of genetic selection for milk yield in dairy cattle have been well documented beginning in the 1960s (https://www.cdcb.us/eval/summary/trend.cfm). For example, Holstein cattle have increased in genetic merit for milk production by 8183 pounds annually, a ˜59% increase. Changes to cow housing, feeding, and management have increased milk yield by an additional 5517 pounds, with the combined effect of genetic selection and management resulting in an approximate doubling of milk yield in five decades. Most species have less organized genetic selection programs than dairy cattle.
Nevertheless, milk yield in goats was reported to increase from 1 to 20 pounds per year, depending on breed, due to genetic selection (http://aipl.arsusda.gov/reference/goat/goatsfs.html).
The effect of improved genetic potential for milk yield can also be demonstrated in non-dairy animals. Genetic selection in Angus beef cattle has increased calf weaning weight by 56 pounds since 1980 (http://www.angus.org/Nce/GeneticTrends.aspx); of that, it is estimated that 24 pounds are due to higher milk production by calves' mothers. In Yorkshire swine, the weight of a typical sow's litter by 21-days has increased by 13 pounds which requires a large increase in milk production (https://mail.nationalswine.com:8443/newstages/TraitLeaderReports.aspx).
While highly successful, the genes and physiological processes which have been altered to facilitate such increases remain elusive. A notable exception is a binucleotide substitution in the DGAT1 gene of dairy cattle that causes a lysine to alanine substitution at position 232 (K232A) (Riquet et al., 1999; Grisart et al., 2002). The alanine variant results in higher milk and protein yields, but is not economically advantageous in many markets because of a substantial correlated decline in milk-fat yield. Mutations in the same gene also influences milk-fat production of buffalo (Bubalus bubalis) (Cardoso et al., 2015), may alter meat quality in swine (Li et al., 2013), and carcass characteristics in beef cattle (Tait et al., 2014). Much genetic research has focused on the identification of QTL (quantitative trait loci), such as DGAT1, with strong influences on performance. However, there has been little effort expended toward identifying epigenetic-QTL. One theory is that genetic selection may act partly through altered epigenetic profiles as DNA sequence variation is reported to cause shifts in DNA methylation (Schübeler, 2015).
More recently, animal industries have incorporated genotyping of single nucleotide polymorphisms (SNP) into genetic selection programs (http://www.illumina.com/products/by-type/microarray-kits.html). Genomic predictions of genetic merit for a variety of traits are facilitated by marker genotypes for thousands of loci spread across the genome (VanRaden et al., 2009). Genomic analysis has largely confirmed the quantitative model of many small effects that cumulatively result in a high degree of variation (Cole et al., 2009), but understanding of how selection alters performance remains elusive.
It is clear that genetic selection has been successful in improving animal performance, but there are many animals for which their estimates of genetic merit fail to correspond to actual phenotypic performance. This has been largely attributed to “preferential treatment” by many authors (Bolgiano et al., 1979; Kuhn et al., 1994; Powell et al., 1994; Weigel et al., 1994; Kuhn and Freeman, 1995; Kuhn et al., 1999). Preferential treatment occurs when an animal is provided with an advantageous environment, a higher plane of nutrition for instance, compared with its contemporaries. The estimate of an animal's genetic merit is thought to then be inflated because of the effect of preferential treatment rather than a true genetic difference. Recently, adjustments were made to deflate genetic evaluations from elite cows in an effort to reduce potential bias from preferential treatment (Wiggans et al., 2011).
While preferential treatment of more valuable animals could bias genetic evaluation to some degree, farmers have economic incentive to maximize performance from all animals and not a selected few. This makes widespread preferential treatment less likely and calls for alternative explanations of mismatches between genetic predictions of performance and actual performance.
There is strong evidence that mechanisms other than preferential treatment deviate performance from expectations based on traditional genetic evaluations. It was previously demonstrated that heritability estimated through female lineages is higher than that estimated through male lineages (Dechow and Norman, 2007); this implies that there are inherited maternal genetic effects that are not fully captured by the additive genetic relationship model that underlies current genetic and genomic evaluation systems. Such effects are also apparent in crossbreeding studies and are often attributed to “cytoplasmic” or “mitochondrial” effects (Schutz et al., 1992; McAllister, 2002). A strong maternal influence independent of variation arising from DNA sequence differences among animals would create that appearance of inflated female genetic evaluations.
Epigenetic modifications may be the molecular mechanism that underlies much of what is perceived as “preferential treatment”, “cytoplasmic”, or “mitochondrial effects”. The effects of DNA methylation on the performance of animal clones (Akagi et al., 2013) has long been recognized, and papers have speculated that epigenetic modifications could alter animal performance (Roche et al., 2009; Couldrey and Cave, 2014). While SNP based genotyping chips are available for many members of Bovidae and ungulate species, DNA methylation chips are not available even though such technology has been developed for humans. Similarly, a pubmed search for QTL identifies matches for many ungulates including cattle, swine, buffalo, goats, sheep, and horse. However, a search for epiallele or epigenetic-QTL provides no results for these species; they are provided by human and plant searches. No sites of differential methylation associated with high or low milk yield have been identified to date.
Differential methylation associated with other important phenotypic characteristics have also not been identified. Of particular interest would be linkages between epigenetic variation and the health of cows. Selection for higher production (Shook, 1989), fertility (VanRaden et al., 2004), and modern management practices (Dechow et al., 2011) have been shown to degrade animal health and wellbeing.
There is a need in the art for a method capable of identifying animals with high performance that cannot otherwise be identified with current genetic and genotyping methods alone. The present invention addresses this unmet need. There is a need in the art for a method capable of identifying dairy cows and other dairy-producing livestock with high milk, fat and protein yields that cannot otherwise be identified with genetics alone. The present invention addresses this unmet need.